The invention pertains to machine vision and, more particularly, to methods for identifying extrema of objects in rotated coordinate frames.
In automated manufacturing, it is often important to determine the location, shape, size and/or angular orientation of an object being processed or assembled. For example, in automated circuit assembly, the precise location of a printed circuit board must be determined before conductive leads can be soldered to it.
Among the important characteristics of an object are the extreme pixels, or extrema, in an image showing the object. The extrema are typically defined as the leftmost, rightmost, uppermost and lowermost points of the object with respect to the reference frame of the image. Together, these points define a rectangle, or bounding box, that wholly encompasses the object. The bounding box is often used to confine the region of the image that must be analyzed to identify detailed characteristics of the object. For example, in an image of multiple semiconductor chips, a bounding box can be used to limit a search for defects in one of them.
Many automated manufacturing systems use the machine vision technique of "blob analysis" to determine geometric properties of an object in an image, such as its angular orientation (or principal moments) and its extrema. A shortcoming of conventional blob analysis techniques is their dependence on the reference frame of the image (which, in turn, is dependent on the reference frame of the image acquisition equipment). This dependence causes the techniques to report large bounding boxes for objects that are rotated with respect to that reference frame. In images with multiple adjacent rotated objects, each bounding box may include an object and portions of its neighbors. This confounds attempts to analyze each object separately.
Prior art attempts to find extrema of objects with respect to rotated reference frames, e.g., as defined by the angular orientation of the objects themselves, have proven unduly time consuming. Typically, these attempts involve applying a conventional blob analysis tool in order to find the principal axes of the object, rotating the image to counter the object's rotation, reapplying the blob analysis tool to find the extrema of the object in the rotated image. Since image rotation is a slow operation, finding the smallest bounding boxes aligned to an object can take an excessively long time.
An object of this invention is to provide improved methods for machine vision and, more particularly, improved methods for identifying extrema of objects in an image.
Still more particularly, an object of the invention is to provide machine vision methods for finding extrema of an object with respect to a rotated reference frame.
Other objects of the invention include providing such machine vision methods as can be readily implemented on existing machine vision processing equipment.
Still other objects are to provide such methods as can be implemented for rapid execution and without excessive consumption of computational power.